通过“nutch与起点R3集成之笔记（一、二、三）”中的步骤，我们可以建立起一个行业内部网的搜索引擎，但搜索引擎一个最重要的功能，就是必须能搜索到网络中最新的内容。这就要求nutch能及时采集到网络中的最新网页信息，同时将这些新采集到的信息更新到solr索引库中。故本篇介绍如何实现索引的更新和去重问题。
我们随时可以使用nutch的crawl命令来爬行网站，例如，今天上午8：00采集一次新浪网站http://www.sina.com.cn，并通过nutch 的solrindex索引到solr索引库中，然后上午10:00再采集一次新浪网站，再通过solrindex索引到solr库中。这样做，可以保证用户能搜索到网络中较新的信息。在solr中实现没有什么问题，因为solr使用的是update方式更新索引库，因为索引field名为id的是主键，只要id是唯一的，update到索引库的操作就是成功的。
我们回过头来看看 《nutch与起点R3集成之笔记（二）》，在该篇中，我们是定义了用digest作为id，而没有采用在nutch的conf 的solrconfig.xml中定义用url作为id。其原因在于，如果用url作为id，在起点R3中会出现看不到索引数据等一些问题，我分析了很久，估计是由于url中会出现： / ? & 字符,这些字符不通过escape转换，在solr的q串提交会出现问题。
我们来看一个solr的查询日志：
[search] webapp=null path=/select params={hl.snippets=3&q=id%3A20679dc38f64730579a1b2538727f76f&hl.simple.pre=%3Cfont+color%3D%27red%27%3E&hl.simple.post=%3C%2Ffont%3E&hl.fl=title&hl.fl=text&hl.usePhraseHighlighter=false&hl=true} hits=1 status=0 QTime=2其中：q=id%3A20679dc38f64730579a1b2538727f76f 是表示指定ID的查询串为 20679dc38f64730579a1b2538727f76f 的查询，如果用url作为id，而url也存在&字符，比如这样会导致将url中&后接内容作为查询参数，会导致不能正确出现查询结果。所以，在nutch向solr进行索引时，用url定义为ID是不合适的。
nutch中digest是对采集的每一个网页内容的32位哈希值，如果两个网页内容完全一样，它们的digest值肯定会一样，但哪怕其中之一多或少一个空格，它们的digest值就会不一样。所以，我认为，用digest做id是一个非常不错的选择。
如果nutch在两次不同的时间抓某个网页，例如还有新浪首页http://www.sina.com.cn，如果在两次抓取这段时间，首页没有什么变化，nutch计算出的两次抓取的首页的digest肯定是一样的，这样，nutch在第二次向solr进行索引时，发现digest在solr的索引库中已存在（id是唯一的），自然就写不进去。这样，避免了索引库的重复记录。
同时，采用digest作为Id，也避免了一个行业内部网有镜像网站在solr索引库的重复记录。
总之，采用digest作为Id，防止了nutch在向solr索引库写入时写入重复记录的问题，有天然去重功能。
但是，还必须解决一个问题：如果nutch在两次不同的时间抓某个网页，如果这个网页有变化，两次的digest值肯定不同，这样，nutch在向solr索引库update时，会将同一个url（如http://www.sina.com.cn)写入两条记录。那么，在搜索界面键入：新浪，肯定会出现两条新浪网站数据。如此类推，nutch采集了同一url网页 n 次，就有可能在solr索引库中有n条相似的记录。这样的情况出现让我很抓狂，陷于困局之中。
其实，这就是对同一个url进行去重的问题。nutch有一个去重的模块org.apache.nutch.indexer.solr.SolrDeleteDuplicates.java，命令为： bin/nutch solrdedup，分析发现，它是对digest相同的记录进行去重。估计是nutch把url作为solr的主键id，所以没有考虑到对url相同的记录进行去重。代码如下：
package org.apache.nutch.indexer.solr;
import java.io.DataInput;
import java.io.DataOutput;
import java.io.IOException;
import java.net.MalformedURLException;
import java.text.SimpleDateFormat;
import java.util.Iterator;
import java.util.Date;
import org.apache.commons.logging.Log;
import org.apache.commons.logging.LogFactory;
import org.apache.hadoop.conf.Configuration;
import org.apache.hadoop.io.Text;
import org.apache.hadoop.io.Writable;
import org.apache.hadoop.mapred.InputFormat;
import org.apache.hadoop.mapred.InputSplit;
import org.apache.hadoop.mapred.JobClient;
import org.apache.hadoop.mapred.JobConf;
import org.apache.hadoop.mapred.OutputCollector;
import org.apache.hadoop.mapred.RecordReader;
import org.apache.hadoop.mapred.Reducer;
import org.apache.hadoop.mapred.Reporter;
import org.apache.hadoop.mapred.lib.IdentityMapper;
import org.apache.hadoop.mapred.lib.NullOutputFormat;
import org.apache.hadoop.util.Tool;
import org.apache.hadoop.util.ToolRunner;
import org.apache.nutch.util.NutchConfiguration;
import org.apache.nutch.util.NutchJob;
import org.apache.nutch.util.TimingUtil;
import org.apache.solr.client.solrj.SolrQuery;
import org.apache.solr.client.solrj.SolrServer;
import org.apache.solr.client.solrj.SolrServerException;
import org.apache.solr.client.solrj.impl.CommonsHttpSolrServer;
import org.apache.solr.client.solrj.request.UpdateRequest;
import org.apache.solr.client.solrj.response.QueryResponse;
import org.apache.solr.common.SolrDocument;
import org.apache.solr.common.SolrDocumentList;
/**
* Utility class for deleting duplicate documents from a solr index.
*
* The algorithm goes like follows:
*
* Preparation:
* <ol>
* <li>Query the solr server for the number of documents (say, N)</li>
* <li>Partition N among M map tasks. For example, if we have two map tasks
* the first map task will deal with solr documents from 0 - (N / 2 - 1) and
* the second will deal with documents from (N / 2) to (N - 1).</li>
* </ol>
*
* MapReduce:
* <ul>
* <li>Map: Identity map where keys are digests and values are {@link SolrRecord}
* instances(which contain id, boost and timestamp)</li>
* <li>Reduce: After map, {@link SolrRecord}s with the same digest will be
* grouped together. Now, of these documents with the same digests, delete
* all of them except the one with the highest score (boost field). If two
* (or more) documents have the same score, then the document with the latest
* timestamp is kept. Again, every other is deleted from solr index.
* </li>
* </ul>
*
* Note that unlike {@link DeleteDuplicates} we assume that two documents in
* a solr index will never have the same URL. So this class only deals with
* documents with <b>different</b> URLs but the same digest.
*/
public class SolrDeleteDuplicates
implements Reducer<Text, SolrDeleteDuplicates.SolrRecord, Text, SolrDeleteDuplicates.SolrRecord>,
Tool {
public static final Log LOG = LogFactory.getLog(SolrDeleteDuplicates.class);
private static final String SOLR_GET_ALL_QUERY = SolrConstants.ID_FIELD + ":[* TO *]";
private static final int NUM_MAX_DELETE_REQUEST = 1000;
public static class SolrRecord implements Writable {
private float boost;
private long tstamp;
private String id;
public SolrRecord() { }
public SolrRecord(SolrRecord old) {
this.id = old.id;
this.boost = old.boost;
this.tstamp = old.tstamp;
}
public SolrRecord(String id, float boost, long tstamp) {
this.id = id;
this.boost = boost;
this.tstamp = tstamp;
}
public String getId() {
return id;
}
public float getBoost() {
return boost;
}
public long getTstamp() {
return tstamp;
}
public void readSolrDocument(SolrDocument doc) {
id = (String)doc.getFieldValue(SolrConstants.ID_FIELD);
boost = (Float)doc.getFieldValue(SolrConstants.BOOST_FIELD);
Date buffer = (Date)doc.getFieldValue(SolrConstants.TIMESTAMP_FIELD);
tstamp = buffer.getTime();
}
public void readFields(DataInput in) throws IOException {
id = Text.readString(in);
boost = in.readFloat();
tstamp = in.readLong();
}
public void write(DataOutput out) throws IOException {
Text.writeString(out, id);
out.writeFloat(boost);
out.writeLong(tstamp);
}
}
public static class SolrInputSplit implements InputSplit {
private int docBegin;
private int numDocs;
public SolrInputSplit() { }
public SolrInputSplit(int docBegin, int numDocs) {
this.docBegin = docBegin;
this.numDocs = numDocs;
}
public int getDocBegin() {
return docBegin;
}
public int getNumDocs() {
return numDocs;
}
public long getLength() throws IOException {
return numDocs;
}
public String[] getLocations() throws IOException {
return new String[] {} ;
}
public void readFields(DataInput in) throws IOException {
docBegin = in.readInt();
numDocs = in.readInt();
}
public void write(DataOutput out) throws IOException {
out.writeInt(docBegin);
out.writeInt(numDocs);
}
}
public static class SolrInputFormat implements InputFormat<Text, SolrRecord> {
/** Return each index as a split. */
public InputSplit[] getSplits(JobConf job, int numSplits) throws IOException {
SolrServer solr = new CommonsHttpSolrServer(job.get(SolrConstants.SERVER_URL));
final SolrQuery solrQuery = new SolrQuery(SOLR_GET_ALL_QUERY);
solrQuery.setFields(SolrConstants.ID_FIELD);
solrQuery.setRows(1);
QueryResponse response;
try {
response = solr.query(solrQuery);
} catch (final SolrServerException e) {
throw new IOException(e);
}
int numResults = (int)response.getResults().getNumFound();
int numDocsPerSplit = (numResults / numSplits);
int currentDoc = 0;
SolrInputSplit[] splits = new SolrInputSplit[numSplits];
for (int i = 0; i < numSplits - 1; i++) {
splits[i] = new SolrInputSplit(currentDoc, numDocsPerSplit);
currentDoc += numDocsPerSplit;
}
splits[splits.length - 1] = new SolrInputSplit(currentDoc, numResults - currentDoc);
return splits;
}
public RecordReader<Text, SolrRecord> getRecordReader(final InputSplit split,
final JobConf job,
Reporter reporter)
throws IOException {
SolrServer solr = new CommonsHttpSolrServer(job.get(SolrConstants.SERVER_URL));
SolrInputSplit solrSplit = (SolrInputSplit) split;
final int numDocs = solrSplit.getNumDocs();
SolrQuery solrQuery = new SolrQuery(SOLR_GET_ALL_QUERY);
solrQuery.setFields(SolrConstants.ID_FIELD, SolrConstants.BOOST_FIELD,
SolrConstants.TIMESTAMP_FIELD,
SolrConstants.DIGEST_FIELD);
solrQuery.setStart(solrSplit.getDocBegin());
solrQuery.setRows(numDocs);
QueryResponse response;
try {
response = solr.query(solrQuery);
} catch (final SolrServerException e) {
throw new IOException(e);
}
final SolrDocumentList solrDocs = response.getResults();
return new RecordReader<Text, SolrRecord>() {
private int currentDoc = 0;
public void close() throws IOException { }
public Text createKey() {
return new Text();
}
public SolrRecord createValue() {
return new SolrRecord();
}
public long getPos() throws IOException {
return currentDoc;
}
public float getProgress() throws IOException {
return currentDoc / (float) numDocs;
}
public boolean next(Text key, SolrRecord value) throws IOException {
if (currentDoc >= numDocs) {
return false;
}
SolrDocument doc = solrDocs.get(currentDoc);
String digest = (String) doc.getFieldValue(SolrConstants.DIGEST_FIELD);
key.set(digest);
value.readSolrDocument(doc);
currentDoc++;
return true;
}
};
}
}
private Configuration conf;
private SolrServer solr;
private int numDeletes = 0;
private UpdateRequest updateRequest = new UpdateRequest();
public Configuration getConf() {
return conf;
}
public void setConf(Configuration conf) {
this.conf = conf;
}
public void configure(JobConf job) {
try {
solr = new CommonsHttpSolrServer(job.get(SolrConstants.SERVER_URL));
} catch (MalformedURLException e) {
throw new RuntimeException(e);
}
}
public void close() throws IOException {
try {
if (numDeletes > 0) {
LOG.info("SolrDeleteDuplicates: deleting " + numDeletes + " duplicates");
updateRequest.process(solr);
solr.commit();
}
} catch (SolrServerException e) {
throw new IOException(e);
}
}
public void reduce(Text key, Iterator<SolrRecord> values,
OutputCollector<Text, SolrRecord> output, Reporter reporter)
throws IOException {
SolrRecord recordToKeep = new SolrRecord(values.next());
while (values.hasNext()) {
SolrRecord solrRecord = values.next();
if (solrRecord.getBoost() > recordToKeep.getBoost() ||
(solrRecord.getBoost() == recordToKeep.getBoost() &&
solrRecord.getTstamp() > recordToKeep.getTstamp())) {
updateRequest.deleteById(recordToKeep.id);
recordToKeep = new SolrRecord(solrRecord);
} else {
updateRequest.deleteById(solrRecord.id);
}
numDeletes++;
if (numDeletes >= NUM_MAX_DELETE_REQUEST) {
try {
LOG.info("SolrDeleteDuplicates: deleting " + numDeletes + " duplicates");
updateRequest.process(solr);
} catch (SolrServerException e) {
throw new IOException(e);
}
updateRequest = new UpdateRequest();
numDeletes = 0;
}
}
}
public void dedup(String solrUrl) throws IOException {
SimpleDateFormat sdf = new SimpleDateFormat("yyyy-MM-dd HH:mm:ss");
long start = System.currentTimeMillis();
LOG.info("SolrDeleteDuplicates: starting at " + sdf.format(start));
LOG.info("SolrDeleteDuplicates: Solr url: " + solrUrl);
JobConf job = new NutchJob(getConf());
job.set(SolrConstants.SERVER_URL, solrUrl);
job.setInputFormat(SolrInputFormat.class);
job.setOutputFormat(NullOutputFormat.class);
job.setMapOutputKeyClass(Text.class);
job.setMapOutputValueClass(SolrRecord.class);
job.setMapperClass(IdentityMapper.class);
job.setReducerClass(SolrDeleteDuplicates.class);
JobClient.runJob(job);
long end = System.currentTimeMillis();
LOG.info("SolrDeleteDuplicates: finished at " + sdf.format(end) + ", elapsed: " + TimingUtil.elapsedTime(start, end));
}
public int run(String[] args) throws IOException {
if (args.length != 1) {
System.err.println("Usage: SolrDeleteDuplicates <solr url>");
return 1;
}
dedup(args[0]);
return 0;
}
public static void main(String[] args) throws Exception {
int result = ToolRunner.run(NutchConfiguration.create(),
new SolrDeleteDuplicates(), args);
System.exit(result);
}
}在上面MAPReduce注释说明中，谈到了如何在分布式服务Hadoop的MAPReduce中 实现删除相同的digest算法如下：
1.将相同的digest文档放在一组，然后只留下最高分（boost值）文档，将其它相同的digest文档删除掉（去重）;
2.如果最高分（boost值）有两个以上的文档，则将最新的时戳（timestamp）的文档留下，将其余的删除掉。
分析代码发现， 只需要将代码中的有两个地方的DIGEST_FIELD替换为URL_FIELD ，就可以实现对相同的url去重。修改后，在eclipse中编译，找到主函数org.apache.nutch.indexer.solr.SolrDeleteDuplicates，建立起SolrDeleteDuplicates运行程序：
其对应的自变量设置为：
点击“运行”,在eclipse控制台上出现运行结果：
表示url去重成功。
总结如下：本文提出，在nutch形成solr索引时，采用digest作为ID，避免了nutch采用url作为ID出现的查询不到结果的问题。通过修改nutch去重模块org.apache.nutch.indexer.solr.SolrDeleteDuplicates.java，实现了对url的去重和更新。
来源：oschina
链接：https://my.oschina.net/u/164278/blog/29258